1. Bidirectional LSTM Autoencoder for Sequence Based Anomaly Detection in Cyber Security
- Author
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Ashima Chawla, Brian Lee, Paul Jacob, Sheila Fallon, and This project reported in this paper has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 700071 for the PROTECTIVE project.
- Subjects
business.industry ,Computer science ,Pattern recognition ,Autoencoders ,Autoencoder ,Embeddings ,System call ,Modeling and Simulation ,Anomaly detection ,Artificial intelligence ,Host based intrusion ,business ,Software ,CuDNNLSTM ,Software Research Institute AIT ,Sequence (medicine) - Abstract
Cyber-security is concerned with protecting information, a vital asset in today’s world. The volume of data that is generated can be usefully analyzed when cyber-security systems are effectively implemented with the aid of software support. Our approach is to determine normal behavior of a system based on sequences of system call traces made by the kernel processes in the system. This paper describes a robust and computationally efficient anomaly based host based intrusion detection system using an Encoder-Decoder mechanism. Using CuDNNLSTM networks, it is possible to obtain a set of comparable results with reduced training times. The Bidirectional Encoder and a unidirectional Decoder is trained on normal call sequences in the ADFA-LD dataset. Intrusion Detection is evaluated based on determining the probability of a sequence being reconstructed by the model yes
- Published
- 2019